A Web-based Operation Management System for Distributed Divisional Organizations
Bibliographic record
Abstract
Operation Management is an important and complex task for a divisional structured organization, especially when the divisions are distributed geographically. In most cases, such organizations didn’t not urge all of it’s divisions to use an integrated information system at the very beginning. But with the development and the expanding of the organization, they sometimes found themselves in the trouble of information exchange and almost lost control of their divisions. At such time, however, on one hand the head quarter inquires more detailed information and more business control on the divisions. On the other hand, some divisions are well built and have its own business processes and information systems. It’s impossible for them to rebuild the information system to integrate with the other divisions and the head quarter as well. Operation Management System (OPMGT) enables real-time inspection of the divisions’ operational data and flexible operation evaluation of each division via the Internet and without much change on the other information systems. The OPMGT presented in this paper was originally developed for the head quarter of a distributed divisional based organization to govern the distributed divisions. System analysis, design and implementation of OPMGT are discussed in detail. Having been developed on the basis of eFramework, a J2EE framework, OPMGT is proved to be highly sufficient in operation management of a distributed divisional structured organization, and it may also do some help to integrate information systems in some degree.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.016 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".